# -*- coding: utf-8 -*-
from datetime import timedelta
from utils.operators.cluster_for_spark_sql_operator import SparkSqlOperator
from jms.dm.dm_customer_monitor_detail_dt import jms_dm__dm_customer_monitor_detail_dt
from jms.time_sensor.time_after_05_45 import time_after_05_45

jms_dm__dm_customer_monitor_dt = SparkSqlOperator(
    task_id='jms_dm__dm_customer_monitor_dt',
    email=['guoruiling@jtexpress.com','yl_bigdata@yl-scm.com'],
    name='dm_customer_monitor_dt_{{ execution_date | date_add(1) | cst_ds }}',
    sql='jms/dm/dm_customer_monitor_dt/execute.sql',
    driver_memory='3G' , 
    executor_memory='3G' , 
    executor_cores=2 , 
    num_executors=3 , 
    pool_slots=1,
    conf={'spark.dynamicAllocation.enabled': 'true',  # 动态资源开启
          'spark.shuffle.service.enabled': 'true',  # 动态资源 Shuffle 服务开启
          'spark.dynamicAllocation.maxExecutors'             : 4 ,
          'spark.dynamicAllocation.cachedExecutorIdleTimeout': 120,  # 动态资源自动释放闲置 Executor 的超时时间(s)
          'spark.sql.sources.partitionOverwriteMode': 'dynamic',  # 允许删改已存在的分区
          'spark.executor.memoryOverhead'             : '2G' ,
          'spark.default.parallelism': 600,
          'spark.sql.shuffle.partitions': 600
          },
    hiveconf={'hive.exec.dynamic.partition': 'true',
              'hive.exec.dynamic.partition.mode': 'nonstrict',
              'hive.exec.max.dynamic.partitions.pernode': 200,
              'hive.exec.max.dynamic.partitions': 200
              },
    execution_timeout = timedelta(minutes=30),
)

jms_dm__dm_customer_monitor_dt << [
    jms_dm__dm_customer_monitor_detail_dt,
    time_after_05_45
]
